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            <ul>
<li><a class="reference internal" href="#">Receiver Operating Characteristic (ROC)</a><ul>
<li><a class="reference internal" href="#plot-roc-curves-for-the-multilabel-problem">Plot ROC curves for the multilabel problem</a></li>
<li><a class="reference internal" href="#area-under-roc-for-the-multiclass-problem">Area under ROC for the multiclass problem</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="receiver-operating-characteristic-roc">
<span id="sphx-glr-auto-examples-model-selection-plot-roc-py"></span><h1>Receiver Operating Characteristic (ROC)<a class="headerlink" href="#receiver-operating-characteristic-roc" title="Permalink to this headline">¶</a></h1>
<p>Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.</p>
<p>ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X axis. This means that the top left corner of the plot is
the “ideal” point - a false positive rate of zero, and a true positive rate of
one. This is not very realistic, but it does mean that a larger area under the
curve (AUC) is usually better.</p>
<p>The “steepness” of ROC curves is also important, since it is ideal to maximize
the true positive rate while minimizing the false positive rate.</p>
<p>ROC curves are typically used in binary classification to study the output of
a classifier. In order to extend ROC curve and ROC area to multi-label
classification, it is necessary to binarize the output. One ROC
curve can be drawn per label, but one can also draw a ROC curve by considering
each element of the label indicator matrix as a binary prediction
(micro-averaging).</p>
<p>Another evaluation measure for multi-label classification is
macro-averaging, which gives equal weight to the classification of each
label.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<dl class="simple">
<dt>See also <a class="reference internal" href="../../modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.roc_auc_score</span></code></a>,</dt><dd><p><a class="reference internal" href="plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
</dd>
</dl>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">cycle</span>

<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span><span class="p">,</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_curve</span><span class="p">,</span> <span class="n">auc</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">label_binarize</span>
<span class="kn">from</span> <span class="nn">sklearn.multiclass</span> <span class="kn">import</span> <span class="n">OneVsRestClassifier</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interp</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_auc_score</span>

<span class="c1"># Import some data to play with</span>
<span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>

<span class="c1"># Binarize the output</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">label_binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

<span class="c1"># Add noisy features to make the problem harder</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">random_state</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">200</span> <span class="o">*</span> <span class="n">n_features</span><span class="p">)]</span>

<span class="c1"># shuffle and split training and test sets</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span>
                                                    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="c1"># Learn to predict each class against the other</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">OneVsRestClassifier</span><span class="p">(</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                                 <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">))</span>
<span class="n">y_score</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># Compute ROC curve and ROC area for each class</span>
<span class="n">fpr</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">tpr</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">roc_auc</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
    <span class="n">fpr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">y_score</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
    <span class="n">roc_auc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

<span class="c1"># Compute micro-average ROC curve and ROC area</span>
<span class="n">fpr</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">y_score</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
<span class="n">roc_auc</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">])</span>
</pre></div>
</div>
<p>Plot of a ROC curve for a specific class</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkorange&#39;</span><span class="p">,</span>
         <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;ROC curve (area = </span><span class="si">%0.2f</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="n">roc_auc</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;navy&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;False Positive Rate&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;True Positive Rate&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Receiver operating characteristic example&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower right&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<div class="section" id="plot-roc-curves-for-the-multilabel-problem">
<h2>Plot ROC curves for the multilabel problem<a class="headerlink" href="#plot-roc-curves-for-the-multilabel-problem" title="Permalink to this headline">¶</a></h2>
<p>Compute macro-average ROC curve and ROC area</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># First aggregate all false positive rates</span>
<span class="n">all_fpr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">fpr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]))</span>

<span class="c1"># Then interpolate all ROC curves at this points</span>
<span class="n">mean_tpr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">all_fpr</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
    <span class="n">mean_tpr</span> <span class="o">+=</span> <span class="n">interp</span><span class="p">(</span><span class="n">all_fpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

<span class="c1"># Finally average it and compute AUC</span>
<span class="n">mean_tpr</span> <span class="o">/=</span> <span class="n">n_classes</span>

<span class="n">fpr</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_fpr</span>
<span class="n">tpr</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_tpr</span>
<span class="n">roc_auc</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">])</span>

<span class="c1"># Plot all ROC curves</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">],</span>
         <span class="n">label</span><span class="o">=</span><span class="s1">&#39;micro-average ROC curve (area = </span><span class="si">{0:0.2f}</span><span class="s1">)&#39;</span>
               <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">roc_auc</span><span class="p">[</span><span class="s2">&quot;micro&quot;</span><span class="p">]),</span>
         <span class="n">color</span><span class="o">=</span><span class="s1">&#39;deeppink&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">],</span>
         <span class="n">label</span><span class="o">=</span><span class="s1">&#39;macro-average ROC curve (area = </span><span class="si">{0:0.2f}</span><span class="s1">)&#39;</span>
               <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">roc_auc</span><span class="p">[</span><span class="s2">&quot;macro&quot;</span><span class="p">]),</span>
         <span class="n">color</span><span class="o">=</span><span class="s1">&#39;navy&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>

<span class="n">colors</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">([</span><span class="s1">&#39;aqua&#39;</span><span class="p">,</span> <span class="s1">&#39;darkorange&#39;</span><span class="p">,</span> <span class="s1">&#39;cornflowerblue&#39;</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">),</span> <span class="n">colors</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">tpr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span>
             <span class="n">label</span><span class="o">=</span><span class="s1">&#39;ROC curve of class </span><span class="si">{0}</span><span class="s1"> (area = </span><span class="si">{1:0.2f}</span><span class="s1">)&#39;</span>
             <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">roc_auc</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k--&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;False Positive Rate&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;True Positive Rate&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Some extension of Receiver operating characteristic to multi-class&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower right&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="area-under-roc-for-the-multiclass-problem">
<h2>Area under ROC for the multiclass problem<a class="headerlink" href="#area-under-roc-for-the-multiclass-problem" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference internal" href="../../modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.roc_auc_score</span></code></a> function can be used for
multi-class classification. The multi-class One-vs-One scheme compares every
unique pairwise combination of classes. In this section, we calcuate the AUC
using the OvR and OvO schemes. We report a macro average, and a
prevalence-weighted average.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y_prob</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="n">macro_roc_auc_ovo</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">multi_class</span><span class="o">=</span><span class="s2">&quot;ovo&quot;</span><span class="p">,</span>
                                  <span class="n">average</span><span class="o">=</span><span class="s2">&quot;macro&quot;</span><span class="p">)</span>
<span class="n">weighted_roc_auc_ovo</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">multi_class</span><span class="o">=</span><span class="s2">&quot;ovo&quot;</span><span class="p">,</span>
                                     <span class="n">average</span><span class="o">=</span><span class="s2">&quot;weighted&quot;</span><span class="p">)</span>
<span class="n">macro_roc_auc_ovr</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">multi_class</span><span class="o">=</span><span class="s2">&quot;ovr&quot;</span><span class="p">,</span>
                                  <span class="n">average</span><span class="o">=</span><span class="s2">&quot;macro&quot;</span><span class="p">)</span>
<span class="n">weighted_roc_auc_ovr</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">multi_class</span><span class="o">=</span><span class="s2">&quot;ovr&quot;</span><span class="p">,</span>
                                     <span class="n">average</span><span class="o">=</span><span class="s2">&quot;weighted&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;One-vs-One ROC AUC scores:</span><span class="se">\n</span><span class="si">{:.6f}</span><span class="s2"> (macro),</span><span class="se">\n</span><span class="si">{:.6f}</span><span class="s2"> &quot;</span>
      <span class="s2">&quot;(weighted by prevalence)&quot;</span>
      <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">macro_roc_auc_ovo</span><span class="p">,</span> <span class="n">weighted_roc_auc_ovo</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;One-vs-Rest ROC AUC scores:</span><span class="se">\n</span><span class="si">{:.6f}</span><span class="s2"> (macro),</span><span class="se">\n</span><span class="si">{:.6f}</span><span class="s2"> &quot;</span>
      <span class="s2">&quot;(weighted by prevalence)&quot;</span>
      <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">macro_roc_auc_ovr</span><span class="p">,</span> <span class="n">weighted_roc_auc_ovr</span><span class="p">))</span>
</pre></div>
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